data/md_simulation/eval_mae.py

###########################################################################################
# Script for evaluating configurations contained in an xyz file with a trained model for
# energy and force MAE
# This program is distributed under the MIT License (see MIT.md)
###########################################################################################

import argparse

# import ase.data
import ase.io
import torch
from mace import data
from mace.tools import torch_geometric, torch_tools, utils

import matplotlib.pyplot as plt
from sklearn.metrics import r2_score

""" python /home/civil/phd/cez218288/scratch/mace_v_0.3.5/md_simulation/mace/eval_mae.py --configs "/home/civil/phd/cez218288/Benchmarking/MDBENCHGNN/example/lips_1/data/test/botnet.xyz" --model "/scratch/scai/phd/aiz238703/MDBENCHGNN/Repulsive/OutputZBL1/MACE_model_500_lips_ZBL1_swa.model"  --device cuda"""


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--configs", help="path to XYZ configurations", required=True)
    parser.add_argument("--model", help="path to model", required=True)
    # parser.add_argument("--output",help="output path",required=True)
    parser.add_argument(
        "--device",
        help="select device",
        type=str,
        choices=["cpu", "cuda"],
        default="cpu",
    )
    parser.add_argument(
        "--default_dtype",
        help="set default dtype",
        type=str,
        choices=["float32", "float64"],
        default="float64",
    )
    parser.add_argument("--batch_size", help="batch size", type=int, default=1)
    parser.add_argument(
        "--info_prefix",
        help="prefix for energy, forces and stress keys",
        type=str,
        default="MACE_",
    )

    return parser.parse_args()


def plot_r2_score(actual, pred, title="Title"):
    save_dir = "./"  # Ensure this path ends with a slash

    # Calculate R² score
    r2 = r2_score(actual, pred)

    # Create the scatter plot
    plt.scatter(actual, pred)
    plt.xlabel("Actual", fontsize=20, fontweight="bold")
    plt.ylabel("Predicted", fontsize=20, fontweight="bold")
    plt.xticks(fontsize=20, fontweight="bold")
    plt.yticks(fontsize=20, fontweight="bold")

    # Plot the 45-degree line
    min_val = min(min(actual), min(pred))
    max_val = max(max(actual), max(pred))
    plt.plot([min_val, max_val], [min_val, max_val], color="red", linestyle="--")
    plt.title(title, fontsize=20, fontweight="bold")

    # Annotate the R² score on the plot
    plt.text(
        0.05,
        0.95,
        f"R² = {r2:.5f}",
        transform=plt.gca().transAxes,
        fontsize=12,
        verticalalignment="top",
    )

    # Save the plot to the specified location with the title in the filename
    filename = f"{title.replace(' ', '_')}.png"
    plt.savefig(f"{save_dir}{filename}")

    # Show the plot
    plt.show()
    # Clear the current figure to avoid overlap
    plt.clf()


def main():
    args = parse_args()
    torch_tools.set_default_dtype(args.default_dtype)
    device = torch_tools.init_device(args.device)

    # Load model
    model = torch.load(f=args.model, map_location=args.device).to(device)
    model = model.double()

    # Load data and prepare input
    atoms_list = ase.io.read(args.configs, index=":")
    configs = [data.config_from_atoms(atoms) for atoms in atoms_list]

    z_table = utils.AtomicNumberTable([int(z) for z in model.atomic_numbers])

    data_loader = torch_geometric.dataloader.DataLoader(
        dataset=[
            data.AtomicData.from_config(
                config, z_table=z_table, cutoff=float(model.r_max)
            )
            for config in configs
        ],
        batch_size=args.batch_size,
        shuffle=False,
        drop_last=False,
    )

    # Collect data

    # Create counter variables
    counter = 0
    e_mae = 0
    f_mae = 0
    e_rmse = 0
    f_rmse = 0

    Predictions_Fx = []
    Actuals_Fx = []

    Predictions_Fy = []
    Actuals_Fy = []

    Predictions_Fz = []
    Actuals_Fz = []
    for batch in data_loader:
        counter += 1
        batch = batch.to(device)
        output = model(batch.to_dict())
        # temp_e1 = abs(batch["energy"]).mean()
        # temp_f1 = abs(batch["forces"]).mean()

        temp_e = (abs(batch["energy"] - output["energy"])).mean()
        temp_f = (abs(batch["forces"] - output["forces"])).mean()
        temp_re = torch.sqrt(((batch["energy"] - output["energy"]) ** 2).mean())

        temp_rf = torch.sqrt(((batch["forces"] - output["forces"]) ** 2).mean())
        Pred_Forces = output["forces"]
        Actual_Forces = batch["forces"]

        Predictions_Fx += Pred_Forces[:, 0].reshape(-1).detach().cpu().numpy().tolist()
        Actuals_Fx += Actual_Forces[:, 0].reshape(-1).detach().cpu().numpy().tolist()

        Predictions_Fy += Pred_Forces[:, 1].reshape(-1).detach().cpu().numpy().tolist()
        Actuals_Fy += Actual_Forces[:, 1].reshape(-1).detach().cpu().numpy().tolist()

        Predictions_Fz += Pred_Forces[:, 2].reshape(-1).detach().cpu().numpy().tolist()
        Actuals_Fz += Actual_Forces[:, 2].reshape(-1).detach().cpu().numpy().tolist()

        counter += 1
        if counter > 500:
            break

        # print("Batch: ",counter,"\te_mae: ",round((temp_e-temp_e1).item(),3),"\tf_mae: ",round((temp_f-temp_f1).item(),3))
        print(
            "Batch_old: ",
            counter,
            "\te_mae: ",
            round((temp_e).item(), 3),
            "\tf_mae: ",
            round((temp_f).item(), 3),
        )

        e_mae += temp_e  # -temp_e1
        f_mae += temp_f  # -temp_f1

        e_rmse += temp_re  # -temp_e1
        f_rmse += temp_rf  # -temp_f1

    print("||Final Results:||")
    print(
        "E_MAE: ",
        round((e_mae / counter).item(), 3),
        "\t F_MAE: ",
        round((f_mae / (counter)).item(), 3),
    )
    print(
        "E_RMSE: ",
        round((e_rmse / counter).item(), 3),
        "\t F_RMSE: ",
        round((f_rmse / (counter)).item(), 3),
    )

    plot_r2_score(Actuals_Fx, Predictions_Fx, "UpstreamMacelips_Fx")
    plot_r2_score(Actuals_Fy, Predictions_Fy, "UpstreamMacelips_Fy")
    plot_r2_score(Actuals_Fz, Predictions_Fz, "UpstreamMacelips_Fz")


if __name__ == "__main__":
    main()